Users may utilize search engines to perform search tasks and/or view recommendations generated for the user based upon the search tasks. In an example, a user may perform one or more searches by generating queries (e.g., “WWII books,” “World War II book review,” and/or other queries) to achieve a goal (e.g., a goal to find a book about World War II). The search engines may attempt to assist the users with the search tasks by utilizing search logs containing previous queries of the user (e.g., a query level and/or a course session level approach). For example, the search engine may utilize the search logs generated during a search session (e.g., searches performed by the user within a time period) to assist the users with a search (e.g., by identifying search results, ranking search results, generating query recommendations, etc). Unfortunately, the query level and/or the course session level approach may have limited accuracy in predicting the user's desires, especially over extended periods of time (e.g., weeks, months, years, etc). Satisfaction metrics (e.g., related to a search relevance of results and/or search performance evaluations) associated with search results generated utilizing the query level and/or the course session level approach may be correspondingly low based upon the limited accuracy. The user may be engaged in a complex search (e.g., planning a vacation involving finding hotels, car rentals, air flights, activities, etc.). For example, the user may generate multiple related queries interspersed with random queries (e.g., the user may multi-task). In an example, the multiple related queries may be associated with researching new refrigerators (e.g., “types of refrigerators,” “refrigerator sellers,” “warranties,” and/or other queries related to refrigerators). The queries related to refrigerators may be interspersed with the random queries (e.g., “what is the weather like today,” “football scores,” etc.).
The random queries may create ambiguity when used to identify a search task. The ambiguity may result in incorrect queries being associated with the search task. Thus, users may be provided with irrelevant query recommendations, unwanted search results, and/or be required to perform multiple searches to obtain desired search results. Unfortunately, because many computing devices, systems, etc., may not have an ability to parse related queries from random queries, user satisfaction metrics based upon an accuracy of identifying related queries may not be determined.